A career decision difference analysis method based on data mining
By initializing the career decision data table in big data mining, creating an opportunity cost quantification sub-table, and constructing a causal index structure, the problems of dynamic quantification storage of opportunity costs and causal equilibrium retrieval are solved, improving the real-time performance and accuracy of the analysis report.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-21
- Publication Date
- 2026-07-17
AI Technical Summary
In existing technologies for big data mining, opportunity costs are difficult to dynamically quantify and store, and causal equilibrium group retrieval lacks underlying index support, affecting the real-time performance and accuracy of analysis reports.
By initializing the core data table for career decision-making, defining the basic structure of personal attributes and decision-making behavior fields, creating a sub-table for quantifying opportunity costs, obtaining industry salary and regional employment index parameters, running Monte Carlo simulations, generating an enhanced career decision-making data table, and constructing a causal index structure using propensity score residuals as the index key, dynamic quantitative storage and causal inference are achieved.
It achieves structured storage of opportunity cost at the database relational schema level, reduces computational complexity, ensures causal equilibrium constraints, and improves the real-time performance and accuracy of analysis reports.
Smart Images
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